Subspace clustering

By Hans-Peter Kriegel, Peer Kröger, Arthur Zimek. WIREs Data Mining Knowl. Discov., 2012

896 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b23d572c7f34fdfa8ce6c548f6d8119d79d545d1

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In Signal Processing

Citations in the corpus

Video Processing From Electro-Optical Sensors for Object Detection and Tracking in a Maritime Environment: A Survey (2016, background): “Thus, subspaces based approaches have been found useful [185] for compact representation of features at pixels before learning is performed.”
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture (2018, background): “• Deep Subspace Clustering Networks (DSC-Nets): DSC-Nets [37] introduces a novel autoencoder architecture to learn an explicit non-linear mapping that is friendly to subspace clustering [38].”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2013, background): “Subspace clustering (see [5] and references therein) refers to the problem of separating data according to their underlying subspaces and finds numerous applications in image processing (e.”
Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview (2021, neutral): “3061660 finding relevant dimensions spanning a subspace for each cluster [6], [7].”
Structured AutoEncoders for Subspace Clustering (2018, background): “SUBSPACE clustering aims at seeking a collection of implicit subspaces to fit a given unlabeled data set and segmenting them into different groups [1].”
Low rank subspace clustering (LRSC) (2014, methodology): “(2013), http://dx.doi.org/10.1016/ j.patrec.2013.08.006 several applications in computer vision (see Vidal (2011) for a review and comparison of existing methods).”
Multi-view low-rank sparse subspace clustering (2017, background): “For example, two points can have a small distance and lie in diﬀerent subspaces or can be far and still lie in the same subspace [10].”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, background): “For a more detailed review of subspace clustering, interested readers can follow [162].”
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (2020, background): “In recent years, subspace clustering [33] has aroused widespread research interests in unsupervised learning and is successfully exploited to various applications, such as image segmentation [22, 36], motion segmentation [14, 4], image clustering [40, 5, 37, 38, 11], genes expression micro-array…”
Deep Subspace Clustering Networks (2017, methodology): “The resulting methods can then be roughly divided into three categories [42]: factorization methods [7, 17, 44, 29, 16], higher-order model based methods [49, 6, 33, 37], and self-expressiveness based methods [9, 24, 26, 46, 15, 12, 22, 52].”
Robust Subspace Clustering (2013, methodology): “In recent years, numerous algorithms have been developed for subspace clustering and applied to various problems in computer vision/machine learning [53] and data mining [43].”
Generalized Latent Multi-View Subspace Clustering (2020, methodology): “SUBSPACE clustering has been successfully used in numerous applications, especially those involving high-dimensional data [1], [2].”
Multi-Task Multi-View Clustering (2016, background): “As far as we know, there are no existing approaches to the multi-task multi-view clustering problem.”
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit (2015, methodology): “In particular, our conditions are naturally related to those for SSC-BP, which reveal insights about the relationship be-tween these two sparsity-based subspace clustering meth-ods.”
Sparse Subspace Clustering: Algorithm, Theory, and Applications (2012, background): “Index Terms—High-dimensional data, intrinsic low-dimensionality, subspaces, clustering, sparse representation, ‘1-minimization, convex programming, spectral clustering, principal angles, motion segmentation, face clustering Ç”
A survey on unsupervised outlier detection in high‐dimensional numerical data (2012, background): “Besides, even in subspace clustering the monotonicity is based on restrictions that render the usage of results limited [33].”
Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video (2012, background): “Subspace models are of great interest in computer vision for background subtraction [27], object tracking [9, 26], and to represent a single scene under varying illuminations [4, 21].”